- Title
- Credit scoring model based on a novel group feature selection method : the case of Chinese small-sized manufacturing enterprises
- Creator
- Zhang, Zhipeng; Chi, Guotai; Colombage, Sisira; Zhou, Ying
- Date
- 2022
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/180923
- Identifier
- vital:15882
- Identifier
-
https://doi.org/10.1080/01605682.2021.1880295
- Identifier
- ISBN:0160-5682 (ISSN)
- Abstract
- In building a predictive credit scoring model, feature selection is an essential pre-processing step that can improve the predictive accuracy and comprehensibility of models. In this study, we select the optimal feature subset based on group feature selection in lieu of the individual feature selection method, to establish a credit scoring model for small manufacturing enterprises. In our methodology, we first select a group of features using the 0-1 programming method, with the objective function of maximising the Gini coefficient (GINI) of the credit score to identify the possibility of default. Then we introduce constraints to remove any redundant features in the same subset, provided they reflect the same information. Finally, we assign weights to different features according to the Gini coefficient, ensuring that the weight of the features reflects their discriminatory power. Our empirical results show that the selection of a set of features more effectively identifies default status than the individual feature selection approach. Moreover, a rating system with more features does not necessarily have better discriminatory power. As the number of features exceeds the optimum number of features selected, the system's discriminatory ability begins to decrease. © Operational Research Society 2022.
- Publisher
- Taylor and Francis Ltd.
- Relation
- Journal of the Operational Research Society Vol. 73, no. 1 (2022), p. 122-138
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ Operational Research Society 2021
- Subject
- 35 Commerce, Management, Tourism and Services; 46 Information and Computing Sciences; 49 Mathematical Sciences; 0-1 programming; Credit scoring; default discrimination; feature selection; Gini coefficient
- Reviewed
- Funder
- The research is supported by the Key Programs of National Natural Science Foundation of China (71731003), the General Programs of National Natural Science Foundation of China (72071026, 71873103, 71971051 and 71971034), the Youth Programs of National Natural Science Foundation of China (71901055 and 71903019), the Major Projects of National Social Science Foundation of China (18ZDA095). The research is also supported by the Chinese Scholarship Council (CSC). We thank the organizations mentioned above.
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